11 citations · 23 across the 8 of their papers we have counts for
8 papers
How Can I Improve? Using GPT to Highlight the Desired and Undesired Parts of Open-ended Responses
Jionghao Lin, Eason Chen, Zeifei Han +5
Automated explanatory feedback systems play a crucial role in facilitating learning for a large cohort of learners by offering feedback that incorporates explanations, significantl…
Harnessing the Power of Beta Scoring in Deep Active Learning for Multi-Label Text Classification
Wei Tan, Ngoc Dang Nguyen, Lan Du +1
Within the scope of natural language processing, the domain of multi-label text classification is uniquely challenging due to its expansive and uneven label distribution. The compl…
Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?
Ngoc Dang Nguyen, Wei Tan, Lan Du +3
Named entity recognition (NER), a task that identifies and categorizes named entities such as persons or organizations from text, is traditionally framed as a multi-class classific…
Re-weighting Tokens: A Simple and Effective Active Learning Strategy for Named Entity Recognition
Haocheng Luo, Wei Tan, Ngoc Dang Nguyen +1
Active learning, a widely adopted technique for enhancing machine learning models in text and image classification tasks with limited annotation resources, has received relatively…
Using Large Language Models to Provide Explanatory Feedback to Human Tutors
Jionghao Lin, Danielle R. Thomas, Feifei Han +4
Research demonstrates learners engaging in the process of producing explanations to support their reasoning, can have a positive impact on learning. However, providing learners rea…
Robust Educational Dialogue Act Classifiers with Low-Resource and Imbalanced Datasets
Jionghao Lin, Wei Tan, Ngoc Dang Nguyen +6
Dialogue acts (DAs) can represent conversational actions of tutors or students that take place during tutoring dialogues. Automating the identification of DAs in tutoring dialogues…
AUC Maximization for Low-Resource Named Entity Recognition
Ngoc Dang Nguyen, Wei Tan, Wray Buntine +3
Current work in named entity recognition (NER) uses either cross entropy (CE) or conditional random fields (CRF) as the objective/loss functions to optimize the underlying NER mode…
Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios
Ngoc Dang Nguyen, Lan Du, Wray Buntine +2
Domain adaptation is an effective solution to data scarcity in low-resource scenarios. However, when applied to token-level tasks such as bioNER, domain adaptation methods often su…